Philippa Kaur

Mason, G. J. Stereotypies: A critical review. Anim. Behav. 41, 1015–1037 (1991).
Google Scholar 
Cussen, V. A. & Mench, J. A. The relationship between personality dimensions and resiliency to environmental stress in orange-winged Amazon parrots (Amazona amazonica), as indicated by the development of abnormal behaviors. PLoS ONE 10, 1–11 (2015).
Google Scholar 
Clubb, R. & Mason, G. Captivity effects on wide-ranging carnivores. Nature 425, 473–474 (2003).ADS 
CAS 
PubMed 

Google Scholar 
Shepherdson, D., Lewis, K. D., Carlstead, K., Bauman, J. & Perrin, N. Individual and environmental factors associated with stereotypic behavior and fecal glucocorticoid metabolite levels in zoo housed polar bears. Appl. Anim. Behav. Sci. 147, 268–277 (2013).
Google Scholar 
Miller, L. J., Bettinger, T. & Mellen, J. The reduction of stereotypic pacing in tigers (Panthera tigris) by obstructing the view of neighbouring individuals. Anim. Welf. 17, 255–258 (2008).CAS 

Google Scholar 
Bachmann, I., Bernasconi, P., Herrmann, R., Weishaupt, M. A. & Stauffacher, M. Behavioural and physiological responses to an acute stressor in crib-biting and control horses. Appl. Anim. Behav. Sci. 82, 297–311 (2003).
Google Scholar 
Ahola, M. K., Vapalahti, K. & Lohi, H. Early weaning increases aggression and stereotypic behaviour in cats. Sci. Rep. 7, 10412 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Salonen, M. et al. Prevalence, comorbidity, and breed differences in canine anxiety in 13,700 Finnish pet dogs. Sci. Rep. 10, 2962 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Garner, J. P. Stereotypies and other abnormal repetitive behaviors: Potential impact on validity, reliability, and replicability of scientific outcomes. ILAR J. 46, 106–117 (2005).CAS 
PubMed 

Google Scholar 
Tynes, V. V. & Sinn, L. Abnormal repetitive behaviors in dogs and cats. A guide for practitioners. Vet. Clin. North Am. Small Anim. Pract. 44, 543–564 (2014).PubMed 

Google Scholar 
Luescher, A. U. Diagnosis and management of compulsive disorders in dogs and cats. Vet. Clin. North Am. Small Anim. Pract. 33, 253–267 (2003).PubMed 

Google Scholar 
Mason, G., Clubb, R., Latham, N. & Vickery, S. Why and how should we use environmental enrichment to tackle stereotypic behaviour?. Appl. Anim. Behav. Sci. 102, 163–188 (2007).
Google Scholar 
Overall, K. L. & Dunham, A. E. Clinical features and outcome in dogs and cats with obsessive-compulsive disorder: 126 Cases (1989–2000). J. Am. Vet. Med. Assoc. 221, 1445–1452 (2002).PubMed 

Google Scholar 
Tiira, K. et al. Environmental effects on compulsive tail chasing in dogs. PLoS One 7, e41684 (2012).Mason, G. & Rushen, J. Stereotypic Animal Behaviour: Fundamentals and Applications to Welfare 2nd edn. (CABI Publishing, 2006).
Google Scholar 
Moon-Fanelli, A. A., Dodman, N. H., Famula, T. R. & Cottam, N. Characteristics of compulsive tail chasing and associated risk factors in Bull Terriers. J. Am. Vet. Med. Assoc. 238, 883–889 (2011).PubMed 

Google Scholar 
Hewson, C. J., Luescher, U. A. & Ball, R. O. Measuring change in the behavioural severity of canine compulsive disorder: The construct validity of categories of change derived from two rating scales. Appl. Anim. Behav. Sci. 60, 55–68 (1998).
Google Scholar 
Vandeleest, J. J., McCowan, B. & Capitanio, J. P. Early rearing interacts with temperament and housing to influence the risk for motor stereotypy in rhesus monkeys (Macaca mulatta). Appl. Anim. Behav. Sci. 132, 81–89 (2011).PubMed 
PubMed Central 

Google Scholar 
Tang, R. et al. Candidate genes and functional noncoding variants identified in a canine model of obsessive-compulsive disorder. Genome Biol. 15, 25 (2014).
Google Scholar 
Dodman, N. H. et al. A canine chromosome 7 locus confers compulsive disorder susceptibility. Mol. Psychiatry 15, 8–10 (2010).CAS 
PubMed 

Google Scholar 
Jeppesen, L. L., Heller, K. E. & Bildsøe, M. Stereotypies in female farm mink (Mustela vison) may be genetically transmitted and associated with higher fertility due to effects on body weight. Appl. Anim. Behav. Sci. 86, 137–143 (2004).
Google Scholar 
Noh, H. J. et al. Integrating evolutionary and regulatory information with a multispecies approach implicates genes and pathways in obsessive-compulsive disorder. Nat. Commun. 8, 1–13 (2017).CAS 

Google Scholar 
Koran, L. M. Quality of life in obsessive-compulsive disorder. Psychiatr. Clin. North Am. 23, 509–517 (2000).ADS 
CAS 
PubMed 

Google Scholar 
Murray, C. J. & Lopez, A. D. The Global Burden of Disease: A Comprehensive Assessment of Mortality and Disability from Diseases, Injuries, and Risk Factors in 1990 and Projected to 2020 (Harvard School of Public Health, 1996).
Google Scholar 
Calzà, J. et al. Altered cortico-striatal functional connectivity during resting state in obsessive-compulsive disorder. Front. Psychiatry 10, 319 (2019).PubMed 
PubMed Central 

Google Scholar 
Brem, S., Grünblatt, E., Drechsler, R., Riederer, P. & Walitza, S. The neurobiological link between OCD and ADHD. ADHD Atten. Deficit Hyperact. Disord. 6, 175–202 (2014).
Google Scholar 
Stein, D. J., Dodman, N. H., Borchelt, P. & Hollander, E. Behavioral disorders in veterinary practice: Relevance to psychiatry. Compr. Psychiatry 35, 275–285 (1994).CAS 
PubMed 

Google Scholar 
Overall, K. L. Natural animal models of human psychiatric conditions: Assessment of mechanism and validity. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 24, 727–776 (2000).CAS 

Google Scholar 
Flament, M. F. et al. Obsessive compulsive disorder in adolescence: An epidemiological study. J. Am. Acad. Child Adolesc. Psychiatry 27, 764–771 (1988).CAS 
PubMed 

Google Scholar 
Nestadt, G. et al. A family study of obsessive-compulsive disorder. Arch. Gen. Psychiatry 57, 358–363 (2000).CAS 
PubMed 

Google Scholar 
Protopopova, A., Hall, N. J. & Wynne, C. D. L. Association between increased behavioral persistence and stereotypy in the pet dog. Behav. Processes 106, 77–81 (2014).PubMed 

Google Scholar 
Valerius, G., Lumpp, A., Kuelz, A. K., Freyer, T. & Voderholzer, U. Reversal learning as a neuropsychological indicator for the neuropathology of obsessive compulsive disorder? A behavioral study. J. Neuropsychiatry Clin. Neurosci. 20, 210–218 (2008).PubMed 

Google Scholar 
Snyder, H. R., Kaiser, R. H., Warren, S. L. & Heller, W. Obsessive-compulsive disorder is associated with broad impairments in executive function: A meta-analysis. Clin. Psychol. Sci. 3, 301–330 (2015).PubMed 

Google Scholar 
Ogata, N. et al. Brain structural abnormalities in Doberman pinschers with canine compulsive disorder. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 45, 1–6 (2013).
Google Scholar 
Norman, L. J. et al. Structural and functional brain abnormalities in attention-deficit/hyperactivity disorder and obsessive-compulsive disorder: A comparative meta-analysis. JAMA Psychiat. 73, 815–825 (2016).
Google Scholar 
Yalcin, E., Ilcol, Y. O. & Batmaz, H. Serum lipid concentrations in dogs with tail chasing. J. Small Anim. Pract. 50, 133–135 (2009).CAS 
PubMed 

Google Scholar 
Vermeire, S. et al. Serotonin 2A receptor, serotonin transporter and dopamine transporter alterations in dogs with compulsive behaviour as a promising model for human obsessive-compulsive disorder. Psychiatry Res. 201, 78–87 (2012).CAS 
PubMed 

Google Scholar 
Moon-Fanelli, A. A. & Dodman, N. H. Description and development of compulsive tail chasing in terriers and response to clomipramine treatment. J. Am. Vet. Med. Assoc. 212, 1252–1257 (1998).CAS 
PubMed 

Google Scholar 
Irimajiri, M. et al. Randomized, controlled clinical trial of the efficacy of fluoxetine for treatment of compulsive disorders in dogs. J. Am. Vet. Med. Assoc. 235, 705–709 (2009).CAS 
PubMed 

Google Scholar 
Walsh, B. R. A critical review of the evidence for the equivalence of canine and human compulsions. Appl. Anim. Behav. Sci. 234, 105166 (2021).
Google Scholar 
Wright, H. F., Mills, D. S. & Pollux, P. M. J. Development and validation of a psychometric tool for assessing impulsivity in the domestic dog (Canis familiaris). Int. J. Comp. Psychol. 24, 210–225 (2011).
Google Scholar 
Dinwoodie, I. R., Dwyer, B., Zottola, V., Gleason, D. & Dodman, N. H. Demographics and comorbidity of behavior problems in dogs. J. Vet. Behav. 32, 62–71 (2019).
Google Scholar 
Sulkama, S. et al. Canine hyperactivity, impulsivity, and inattention share similar demographic risk factors and behavioural comorbidities with human ADHD. Transl. Psychiatry 11, 501 (2021).PubMed 
PubMed Central 

Google Scholar 
Kooij, J. J. S. et al. Updated European Consensus Statement on diagnosis and treatment of adult ADHD. Eur. Psychiatry 56, 14–34 (2019).CAS 
PubMed 

Google Scholar 
Nakao, T., Okada, K. & Kanba, S. Neurobiological model of obsessive-compulsive disorder: Evidence from recent neuropsychological and neuroimaging findings. Psychiatry Clin. Neurosci. 68, 587–605 (2014).PubMed 

Google Scholar 
Milad, M. R. & Rauch, S. L. Obsessive-compulsive disorder: Beyond segregated cortico-striatal pathways. Trends Cogn. Sci. 16, 43–51 (2012).PubMed 

Google Scholar 
Hollander, E. Managing aggressive behavior in patients with obsessive-compulsive disorder and borderline personality disorder. J. Clin. Psychiatry 60, 38–44 (1999).PubMed 

Google Scholar 
Marsden, M. D. & Wood-Gush, D. G. M. The use of space by group-housed sheep. Appl. Anim. Behav. Sci. 15, 178 (1986).
Google Scholar 
Burn, C. C. A vicious cycle: A cross-sectional study of canine tail-chasing and human responses to it, using a free video-sharing website. PLoS ONE 6, e26553 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Stubbs, B. et al. An examination of the anxiolytic effects of exercise for people with anxiety and stress-related disorders: A meta-analysis. Psychiatry Res. 249, 102–108 (2017).PubMed 

Google Scholar 
Schneider, B. M., Dodman, N. H. & Maranda, L. Use of memantine in treatment of canine compulsive disorders. J. Vet. Behav. Clin. Appl. Res. 4, 118–126 (2009).
Google Scholar 
Mihevc, S. P. & Majdic, G. Canine cognitive dysfunction and Alzheimer’s disease-two facets of the same disease?. Front. Neurosci. 13, 604 (2019).
Google Scholar 
Delorme, R. et al. Admixture analysis of age at onset in obsessive-compulsive disorder. Psychol. Med. 35, 237–243 (2005).PubMed 

Google Scholar 
Flaisher-Grinberg, S. et al. Ovarian hormones modulate ‘compulsive’ lever-pressing in female rats. Horm. Behav. 55, 356–365 (2009).CAS 
PubMed 

Google Scholar 
Fernández-Guasti, A., Agrati, D., Reyes, R. & Ferreira, A. Ovarian steroids counteract serotonergic drugs actions in an animal model of obsessive-compulsive disorder. Psychoneuroendocrinology 31, 924–934 (2006).PubMed 

Google Scholar 
Col, R., Day, C. & Phillips, C. J. C. An epidemiological analysis of dog behavior problems presented to an Australian behavior clinic, with associated risk factors. J. Vet. Behav. Clin. Appl. Res. 15, 1–11 (2016).
Google Scholar 
Rusbridge, C. Neurological diseases of the Cavalier King Charles spaniel. J. Small Anim. Pract. 46, 265–272 (2005).CAS 
PubMed 

Google Scholar 
Wrzosek, M., Płonek, M., Nicpoń, J., Cizinauskas, S. & Pakozdy, A. Retrospective multicenter evaluation of the ‘fly-catching syndrome’ in 24 dogs: EEG, BAER, MRI, CSF findings and response to antiepileptic and antidepressant treatment. Epilepsy Behav. 53, 184–189 (2015).PubMed 

Google Scholar 
Cao, X. et al. Balancing selection on CDH2 may be related to the behavioral features of the Belgian malinois. PLoS ONE 9, e110075 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Moon-Fanelli, A. A., Dodman, N. H. & Cottam, N. Blanket and flank sucking in Doberman Pinschers. J. Am. Vet. Med. Assoc. 231, 907–912 (2007).PubMed 

Google Scholar 
Tiira, K. & Lohi, H. Reliability and validity of a questionnaire survey in canine anxiety research. Appl. Anim. Behav. Sci. 155, 82–92 (2014).
Google Scholar 
Puurunen, J. et al. Inadequate socialisation, inactivity, and urban living environment are associated with social fearfulness in pet dogs. Sci. Rep. 10, 3527 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hakanen, E. et al. Active and social life is associated with lower non-social fearfulness in pet dogs. Sci. Rep. 10, 1–13 (2020).
Google Scholar 
Mikkola, S. et al. Aggressive behaviour is affected by demographic, environmental and behavioural factors in purebred dogs. Sci. Rep. 11, 9433 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hejjas, K. et al. Association of polymorphisms in the dopamine D4 receptor gene and the activity-impulsivity endophenotype in dogs. Anim. Genet. 38, 629–633 (2007).CAS 
PubMed 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. (2019).Hastie, T. gam: Generalized Additive Models. (2018).Robinson, D. & Hayes, A. broom: Convert Statistical Analysis Objects into Tidy Tibbles. https://cran.r-project.org/package=broom (2018).Wickham, H., François, R., Lionel, H. & Müller, K. dplyr: A Grammar of Data Manipulation. https://cran.r-project.org/package=dplyr (2019).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).MATH 

Google Scholar 
Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage Publications, 2011).
Google Scholar 
Robin, X. et al. pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 12, 77 (2011).
Google Scholar 
Lenth, R. emmeans: Estimated Marginal Means, aka Least-Squares Means. https://cran.r-project.org/package=emmeans (2019).Fox, J. Effect Displays in R for Generalised Linear Models. J. Stat. Softw. 8, 1–27 (2003).
Google Scholar 
Goto, A., Arata, S., Kiyokawa, Y., Takeuchi, Y. & Mori, Y. Risk factors for canine tail chasing behaviour in Japan. Vet. J. 192, 445–448 (2012).PubMed 

Google Scholar  More

Andersson, M. Sexual selection (Princeton University Press, 2019).
Google Scholar 
Davy, A. J. & Smith, H. Life-history variation and environment. In Plant Population Ecology (eds Davy, A. J., Hutchings, M. J. & Watkinson, A. R.) 1–22. 28th Symposium of the British Ecological Society, Sussex, 1987 (Blackwell, 1988).Wilson, K. L., De Gisi, J., Cahill, C. L., Barker, O. E. & Post, J. R. Life-history variation along environmental and harvest clines of a northern freshwater fish: plasticity and adaptation. J. Anim. Ecol. 88, 717–733 (2019).PubMed 

Google Scholar 
Laiolo, P. & Obeso, J. R. Life-history responses to the altitudinal gradient. In High Mountain Conservation in a Changing World (eds Catalan, J. et al.) 253–283 (Springer, 2017).
Google Scholar 
Schwarz, R. & Meiri, S. The fast-slow life-history continuum in insular lizards: a comparison between species with invariant and variable clutch sizes. J. Biogeogr. 44, 2808–2815 (2017).
Google Scholar 
Holm, S. et al. Size-related life-history traits in geometrid moths: a comparison of a temperate and a tropical community. Ecol. Entomol. 44, 711–716 (2019).
Google Scholar 
Ferguson, G. W. & Fox, S. F. Annual variation of survival advantage of large juvenile side-blotched lizards, Uta stansburiana: Its causes and evolutionary significance. Evolution 38, 342–349 (1984).PubMed 

Google Scholar 
Madsen, T., Ujvari, B., Shine, R. & Olsson, M. Rain, rats and pythons: Climate-driven population dynamics of predators and prey in tropical Australia. Austral Ecol. 31, 30–37 (2006).
Google Scholar 
Brown, G. P. & Shine, R. Rain, prey and predators: climatically driven shifts in frog abundance modify reproductive allometry in a tropical snake. Oecologia 154, 361–368 (2007).ADS 
PubMed 

Google Scholar 
James, C. & Shine, R. Life-history strategies of Australian lizards: a comparison between the tropics and the temperate zone. Oecologia 75, 307–316 (1988).ADS 
PubMed 

Google Scholar 
Mesquita, D. O. et al. Life-history patterns of lizards of the world. Am. Nat. 187, 689–705 (2016).PubMed 

Google Scholar 
Meiri, S. et al. The global diversity and distribution of lizard clutch sizes. Glob. Ecol. Biogeogr. 29, 1515–1530 (2020).
Google Scholar 
Andrews, R. M. Growth rate in island and mainland anoline lizards. Copeia 1976, 477–482 (1976).
Google Scholar 
Jessop, T. S. et al. Maximum body size among insular Komodo dragon populations covaries with large prey density. Oikos 112, 422–429 (2006).
Google Scholar 
Van Buskirk, J. & Crowder, L. B. Life-history variation in marine turtles. Copeia 1994, 66–81 (1994).
Google Scholar 
Broderick, A. C., Godley, B. J. & Hays, G. C. Trophic status drives interannual variability in nesting numbers of marine turtles. Proc. R. Soc. B 268, 1481–1487 (2001).CAS 
PubMed 
PubMed Central 

Google Scholar 
Broderick, A. C., Glen, F., Godley, B. J. & Hays, G. C. Variation in reproductive output of marine turtles. J. Exp. Mar. Biol. Ecol. 288, 95–109 (2003).
Google Scholar 
Pike, D. A. Climate influences the global distribution of sea turtle nesting. Glob. Ecol. Biogeogr. 22, 555–566 (2013).
Google Scholar 
Ujvari, B., Shine, R., Luiselli, L. & Madsen, T. Climate-induced reaction norms for life-history traits in pythons. Ecology 92, 1858–1864 (2011).PubMed 

Google Scholar 
Shine, R., Shine, T. G., Brown, G. P. & Goiran, C. Life history traits of the sea snake Emydocephalus annulatus, based on a 17-yr study. Coral Reefs 39, 1407–1414 (2020).
Google Scholar 
Shine, R., Brown, G. P. & Goiran, C. Population dynamics of the sea snake Emydocephalus annulatus (Elapidae, Hydrophiinae). Sci. Rep. 11, 20701 (2021). ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Goiran, C., Dubey, S. & Shine, R. Effects of season, sex and body size on the feeding ecology of turtle-headed sea snakes (Emydocephalus annulatus) on IndoPacific inshore coral reefs. Coral Reefs 32, 527–538 (2013).ADS 

Google Scholar 
Ineich, I. & Laboute, P. Les Serpents Marins de Nouvelle-Calédonie (IRD éditions, 2002).Udyawer, V., Goiran, C. & Shine, R. Peaceful coexistence between people and deadly wildlife: Why are recreational users of the ocean so rarely bitten by sea snakes? People Nat. 3, 335–346 (2021).
Google Scholar 
Goiran, C., Brown, G. P. & Shine, R. The behaviour of sea snakes (Emydocephalus annulatus) shifts with the tides. Sci. Rep. 10, 1–8 (2020).
Google Scholar 
Lukoschek, V. & Shine, R. Sea snakes rarely venture far from home. Ecol. Evol. 2, 1113–1121 (2012).PubMed 
PubMed Central 

Google Scholar 
Shine, R., Goiran, C., Shine, T., Fauvel, T. & Brischoux, F. Phenotypic divergence between seasnake (Emydocephalus annulatus) populations from adjacent bays of the New Caledonian lagoon. Biol. J. Linn. Soc. 107, 824–832 (2012).
Google Scholar 
Avolio, C., Shine, R. & Pile, A. J. The adaptive significance of sexually dimorphic scale rugosity in sea snakes. Am. Nat. 167, 728–738 (2006).PubMed 

Google Scholar 
White, G. C. & Burnham, K. P. Program MARK: Survival estimation from populations of marked animals. Bird Study 46, S120–S139 (1999).
Google Scholar 
Russell, B. C., Anderson, G. R. V. & Talbot, F. H. Seasonality and recruitment of coral reef fishes. Mar. Freshw. Res. 28, 521–528 (1977).
Google Scholar 
Shine, R., Bonnet, X., Elphick, M. J. & Barrott, E. G. A novel foraging mode in snakes: browsing by the sea snake Emydocephalus annulatus (Serpentes, Hydrophiidae). Funct. Ecol. 18, 16–24 (2004).
Google Scholar 
Calow, P. Adaptive aspects of energy allocation. In Fish Energetics (eds Tytler, P. & Calow, P.) 13–31 (Springer, 1985).
Google Scholar 
Lenormand, T. Gene flow and the limits to natural selection. Trends Ecol. Evol. 17, 183–189 (2002).
Google Scholar 
Bronikowski, A. M. Experimental evidence for the adaptive evolution of growth rate in the garter snake Thamnophis elegans. Evolution 54, 1760–1767 (2000).CAS 
PubMed 

Google Scholar 
Cook, T. R., Bonnet, X., Fauvel, T., Shine, R. & Brischoux, F. Foraging behaviour and energy budgets of sea snakes from New Caledonia: Insights from implanted data-loggers. J. Zool. 298, 82–93 (2016).
Google Scholar 
Bonnet, X., Brischoux, F., Briand, M. & Shine, R. Plasticity matches phenotype to local conditions despite genetic homogeneity across 13 snake populations. Proc. R. Soc. B 288, 20202916 (2021).PubMed 
PubMed Central 

Google Scholar 
Lukoschek, V., Waycott, M. & Marsh, H. Phylogeography of the olive sea snake, Aipysurus laevis (Hydrophiinae) indicates Pleistocene range expansion around northern Australia but low contemporary gene flow. Mol. Ecol. 16, 3406–3422 (2007).CAS 
PubMed 

Google Scholar 
Nitschke, C. R., Hourston, M., Udyawer, V. & Sanders, K. L. Rates of population differentiation and speciation are decoupled in sea snakes. Biol. Lett. 14, 20180563 (2018).PubMed 
PubMed Central 

Google Scholar 
Heatwole, H., Grech, A., Monahan, J. F., King, S. & Marsh, H. Thermal biology of sea snakes and sea kraits. Integr. Comp. Biol. 52, 257–273 (2012).PubMed 

Google Scholar 
Brischoux, F., Rolland, V., Bonnet, X., Caillaud, M. & Shine, R. Effects of oceanic salinity on body condition in sea snakes. Integr. Comp. Biol. 52, 235–244 (2012).PubMed 

Google Scholar 
Bonnet, X. et al. Spatial variation in age structure among populations of a colonial marine snake: the influence of ectothermy. J. Anim. Ecol. 84, 925–933 (2015).PubMed 

Google Scholar 
Heatwole, H. Sea Snakes 2nd edn. (Krieger Publishing, 1999).
Google Scholar 
Blouin-Demers, G. & Weatherhead, P. J. Thermal ecology of black rat snakes (Elaphe obsoleta) in a thermally challenging environment. Ecology 82, 3025–3043 (2001).
Google Scholar 
Goiran, C., Brown, G. P. & Shine, R. Niche partitioning within a population of sea snakes is constrained by ambient thermal homogeneity and small prey size. Biol. J. Linn. Soc. 129, 644–651 (2020).
Google Scholar 
Lowe, J. R. et al. Regional versus latitudinal variation in the life-history traits and demographic rates of a reef fish, Centropyge bispinosa, in the Coral Sea and Great Barrier Reef Marine Parks, Australia. J. Fish Biol. 99, 1602–1612. https://doi.org/10.1111/jfb.14865 (2021).PubMed 

Google Scholar 
Gust, N., Choat, J. & Ackerman, J. Demographic plasticity in tropical reef fishes. Mar. Biol. 140, 1039–1051 (2002).
Google Scholar 
Kingsford, M. J., Welch, D. & O’Callaghan, M. Latitudinal and cross-shelf patterns of size, age, growth, and mortality of a tropical damselfish Acanthochromis polyacanthus on the Great Barrier Reef. Diversity 11, 67 (2019).
Google Scholar  More

Díaz, S. et al. Set ambitious goals for biodiversity and sustainability. Science 370, 411 (2020).PubMed 

Google Scholar 
Soto-Navarro, C. A. et al. Towards a multidimensional biodiversity index for national application. Nat. Sustain. 4, 933–942 (2021).Skidmore, A. K. et al. Priority list of biodiversity metrics to observe from space. Nat. Ecol. Evol. 5, 896–906 (2021).PubMed 

Google Scholar 
Brum, F. T. et al. Global priorities for conservation across multiple dimensions of mammalian diversity. Proc. Natl Acad. Sci. USA 114, 7641–7646 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Girardello, M. et al. Global synergies and trade-offs between multiple dimensions of biodiversity and ecosystem services. Sci. Rep. 9, 5636 (2019).PubMed 
PubMed Central 

Google Scholar 
Chaplin-Kramer, R. et al. Global modeling of nature’s contributions to people. Science 366, 255–258 (2019).CAS 
PubMed 

Google Scholar 
Pettorelli, N. et al. Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions. Remote Sens. Ecol. Conserv. 2, 122–131 (2016).
Google Scholar 
Paganini, M., Leidner, A. K., Geller, G., Turner, W. & Wegmann, M. The role of space agencies in remotely sensed essential biodiversity variables. Remote Sens. Ecol. Conserv. 2, 132–140 (2016).
Google Scholar 
O’Connor, B. et al. Earth observation as a tool for tracking progress towards the Aichi Biodiversity Targets. Remote Sens. Ecol. Conserv. 1, 19–28 (2015).
Google Scholar 
Skidmore, A. K. et al. Environmental science: agree on biodiversity metrics to track from space. Nature 523, 403–405 (2015).CAS 
PubMed 

Google Scholar 
Reddy, C. S. et al. Remote sensing enabled essential biodiversity variables for biodiversity assessment and monitoring: technological advancement and potentials. Biodivers. Conserv. 30, 1–14 (2021).
Google Scholar 
Vihervaara, P. et al. How essential biodiversity variables and remote sensing can help national biodiversity monitoring. Glob. Ecol. Conserv. 10, 43–59 (2017).
Google Scholar 
Luque, S., Pettorelli, N., Vihervaara, P. & Wegmann, M. Improving biodiversity monitoring using satellite remote sensing to provide solutions towards the 2020 conservation targets. Methods Ecol. Evol. 9, 1784–1786 (2018).
Google Scholar 
Moritz, C. Applications of mitochondrial DNA analysis in conservation: a critical review. Mol. Ecol. 3, 401–411 (1994).CAS 

Google Scholar 
Graham, C. H., Ferrier, S., Huettman, F., Moritz, C. & Peterson, A. T. New developments in museum-based informatics and applications in biodiversity analysis. Trends Ecol. Evol. 19, 497–503 (2004).PubMed 

Google Scholar 
Czyż, E. A. et al. Intraspecific genetic variation of a Fagus sylvatica population in a temperate forest derived from airborne imaging spectroscopy time series. Ecol. Evol. 10, 7419–7430 (2020).PubMed 
PubMed Central 

Google Scholar 
Guillén-Escribà, C. et al. Remotely sensed between-individual functional trait variation in a temperate forest. Ecol. Evol. 11, 10834–10867 (2021).PubMed 
PubMed Central 

Google Scholar 
Hoffmann, A. A. & Sgrò, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).CAS 
PubMed 

Google Scholar 
Shaw, R. G. & Etterson, J. R. Rapid climate change and the rate of adaptation: insight from experimental quantitative genetics. New Phytol. 195, 752–765 (2012).PubMed 

Google Scholar 
Wang, Z. et al. Foliar functional traits from imaging spectroscopy across biomes in the eastern North America. New Phytol. 228, 494–511 (2020).PubMed 

Google Scholar 
Poorter, L. et al. Are functional traits good predictors of demographic rates? Evidence from five neotropical forests. Ecology 89, 1908–1920 (2008).CAS 
PubMed 

Google Scholar 
Cornwell, W. K. & Ackerly, D. D. Community assembly and shifts in plant trait distributions across an environmental gradient in coastal California. Ecol. Monogr. 79, 109–126 (2009).
Google Scholar 
Gao, Q. et al. Stimulation of soil respiration by elevated CO2 is enhanced under nitrogen limitation in a decade-long grassland study. Proc. Natl Acad. Sci. USA 117, 33317–33324 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Urban, M. C. et al. Improving the forecast for biodiversity under climate change. Science 353, aad8466 (2016).PubMed 

Google Scholar 
Hoffmann, A. A. & Sgrò, C. M. Comparative studies of critical physiological limits and vulnerability to environmental extremes in small ectotherms: how much environmental control is needed? Integr. Zool. 13, 355–371 (2018).PubMed 
PubMed Central 

Google Scholar 
Marshall, C. R. A simple method for bracketing absolute divergence times on molecular phylogenies using multiple fossil calibration points. Am. Nat. 171, 726–742 (2008).PubMed 

Google Scholar 
Quental, T. B. & Marshall, C. R. Diversity dynamics: molecular phylogenies need the fossil record. Trends Ecol. Evol. 25, 434–441 (2010).PubMed 

Google Scholar 
Graham, C. H., Moritz, C. & Williams, S. E. Habitat history improves prediction of biodiversity in rainforest fauna. Proc. Natl Acad. Sci. USA 103, 632–636 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).
Google Scholar 
Zipkin, E. F. et al. Addressing data integration challenges to link ecological processes across scales. Front. Ecol. Environ. 19, 30–38 (2021).
Google Scholar 
Cavender-Bares, J. et al. BII-Implementation: the causes and consequences of plant biodiversity across scales in a rapidly changing world. Res. Ideas Outcomes 7, e63850 (2021).
Google Scholar 
Hwang, D. et al. A data integration methodology for systems biology. Proc. Natl Acad. Sci. USA 102, 17296–17301 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
O’Malley, M. A. & Soyer, O. S. The roles of integration in molecular systems biology. Stud. Hist. Philos. Sci. C 43, 58–68 (2012).
Google Scholar 
Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES, 2019).von Humboldt, A. & Bonpland, A. Essai sur la Géographie des Plantes, Accompagné d’un Tableau Physique des Régions Equinoxiales (Levrault & Schoell, 1807).Darwin, C. On the Origin of Species by Means of Natural Selection 6th edn (with corrections and additions to 1872) (John Murray, 1888).Braun, E. L. Deciduous Forests of Eastern North America (Hafner Publishing Company, 1967).Slik, J. W. F. et al. Phylogenetic classification of the world’s tropical forests. Proc. Natl Acad. Sci. USA 115, 1837 (2018).PubMed 
PubMed Central 

Google Scholar 
Tingley, M. W., Monahan, W. B., Beissinger, S. R. & Moritz, C. Birds track their Grinnellian niche through a century of climate change. Proc. Natl Acad. Sci. USA 106, 19637–19643 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wiens, J. J. et al. Niche conservatism as an emerging principle in ecology and conservation biology. Ecol. Lett. 13, 1310–1324 (2010).PubMed 

Google Scholar 
Cavender-Bares, J., Ackerly, D., Hobbie, S. & Townsend, P. Evolutionary legacy effects on ecosystems: biogeographic origins, plant traits, and implications for management in the era of global change. Annu. Rev. Ecol. Evol. Syst. 47, 433–462 (2016).
Google Scholar 
Crisp, M. D., Arroyo, M. T. K., Cook, L. G., Gandolfo, M. A. & Jordan, G. J. Phylogenetic biome conservatism on a global scale. Nature 458, 754–756 (2009).CAS 
PubMed 

Google Scholar 
Forrestel, E. J., Donoghue, M. J. & Smith, M. D. Convergent phylogenetic and functional responses to altered fire regimes in mesic savanna grasslands of North America and South Africa. New Phytol. 203, 1000–1011 (2014).PubMed 

Google Scholar 
Auler, A. S. & Smart, P. L. Late quaternary paleoclimate in semiarid northeastern Brazil from U-series dating of travertine and water-table speleothems. Quat. Res. 55, 159–167 (2001).CAS 

Google Scholar 
Cheng, H. et al. Climate change patterns in Amazonia and biodiversity. Nat. Commun. 4, 1411 (2013).PubMed 

Google Scholar 
Ledru, M.-P. et al. The last 50,000 years in the Neotropics (Southern Brazil): evolution of vegetation and climate. Palaeogeogr. Palaeoclimatol. Palaeoecol. 123, 239–257 (1996).
Google Scholar 
Brown, J. L., Hill, D. J., Dolan, A. M., Carnaval, A. C. & Haywood, A. M. PaleoClim, high spatial resolution paleoclimate surfaces for global land areas. Sci. Data 5, 180254 (2018).PubMed 
PubMed Central 

Google Scholar 
Delsuc, F., Brinkmann, H. & Philippe, H. Phylogenomics and the reconstruction of the tree of life. Nat. Rev. Genet. 6, 361–375 (2005).CAS 
PubMed 

Google Scholar 
Ciccarelli, F. D. et al. Toward automatic reconstruction of a highly resolved tree of life. Science 311, 1283–1287 (2006).CAS 
PubMed 

Google Scholar 
Beck, P. S. A. & Goetz, S. J. Satellite observations of high northern latitude vegetation productivity changes between 1982 and 2008: ecological variability and regional differences. Environ. Res. Lett. 6, 045501 (2011).
Google Scholar 
Kokaly, R. F., Asner, G. P., Ollinger, S. V., Martin, M. E. & Wessman, C. A. Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies. Remote Sens. Environ. 113, S78–S91 (2009).
Google Scholar 
Graham, C. H. et al. The origin and maintenance of montane diversity: integrating evolutionary and ecological processes. Ecography 37, 711–719 (2014).
Google Scholar 
Carnaval, A. C., Hickerson, M. J., Haddad, C. F., Rodrigues, M. T. & Moritz, C. Stability predicts genetic diversity in the Brazilian Atlantic forest hotspot. Science 323, 785–789 (2009).CAS 
PubMed 

Google Scholar 
Dynesius, M. & Jansson, R. Evolutionary consequences of changes in species geographical distributions driven by Milankovitch climate oscillations. Proc. Natl Acad. Sci. USA 97, 9115 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
Carnaval, A. C. et al. Prediction of phylogeographic endemism in an environmentally complex biome. Proc. R. Soc. B 281, 20141461 (2014).PubMed 
PubMed Central 

Google Scholar 
Soudzilovskaia, N. A. et al. Global mycorrhizal plant distribution linked to terrestrial carbon stocks. Nat. Commun. 10, 5077 (2019).PubMed 
PubMed Central 

Google Scholar 
Forest, F., Crandall, K. A., Chase, M. W. & Faith, D. P. Phylogeny, extinction and conservation: embracing uncertainties in a time of urgency. Philos. Trans. R. Soc. Lond. B 370, 20140002 (2015).
Google Scholar 
Faith, D. P. Phylogenetic diversity, functional trait diversity and extinction: avoiding tipping points and worst-case losses. Philos. Trans. R. Soc. Lond. B 370, 20140011 (2015).
Google Scholar 
Violle, C. et al. Let the concept of trait be functional! Oikos 116, 882–892 (2007).
Google Scholar 
Lavorel, S. et al. Assessing functional diversity in the field—methodology matters! Funct. Ecol. 22, 134–147 (2008).
Google Scholar 
Petchey, O. L. & Gaston, K. J. Functional diversity: back to basics and looking forward. Ecol. Lett. 9, 741–758 (2006).PubMed 

Google Scholar 
Lavorel, S. & Garnier, E. Predicting changes in community composition and ecosystem functioning from plant traits: revisiting the Holy Grail. Funct. Ecol. 16, 545–556 (2002).
Google Scholar 
Suding, K. N. et al. Scaling environmental change through the community-level: a trait-based response-and-effect framework for plants. Glob. Change Biol. 14, 1125–1140 (2008).
Google Scholar 
Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).CAS 
PubMed 

Google Scholar 
Reich, P. B., Walters, M. B. & Ellsworth, D. S. From tropics to tundra: global convergence in plant functioning. Proc. Natl Acad. Sci. USA 94, 13730–13734 (1997).CAS 
PubMed 
PubMed Central 

Google Scholar 
Dahlin, K. M., Asner, G. P. & Field, C. B. Environmental and community controls on plant canopy chemistry in a Mediterranean-type ecosystem. Proc. Natl Acad. Sci. USA 110, 6895–6900 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kattge, J. et al. TRY plant trait database—enhanced coverage and open access. Glob. Chang. Biol. 26, 119–188 (2020).PubMed 

Google Scholar 
Enquist, B., Condit, R., Peet, R., Schildhauer, M. & Thiers, B. Cyberinfrastructure for an integrated botanical information network to investigate the ecological impacts of global climate change on plant biodiversity. PeerJ 4, e2615v2612 (2016).
Google Scholar 
Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).PubMed 

Google Scholar 
Asner, G. P., Martin, R. E., Anderson, C. B. & Knapp, D. E. Quantifying forest canopy traits: imaging spectroscopy versus field survey. Remote Sens. Environ. 158, 15–27 (2015).
Google Scholar 
Fajardo, A. & Siefert, A. Phenological variation of leaf functional traits within species. Oecologia 180, 951–959 (2016).PubMed 

Google Scholar 
Townsend, P. A., Foster, J. R., Chastain, R. A. Jr. & Currie, W. S. Application of imaging spectroscopy to mapping canopy nitrogen in the forests of the central Appalachian Mountains using Hyperion and AVIRIS. Geosci. Remote Sens. IEEE Trans. 41, 1347–1354 (2003).
Google Scholar 
Féret, J. B., Gitelson, A. A., Noble, S. D. & Jacquemoud, S. PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle. Remote Sens. Environ. 193, 204–215 (2017).
Google Scholar 
Berger, K. et al. Retrieval of aboveground crop nitrogen content with a hybrid machine learning method. Int. J. Appl. Earth Obs. Geoinf. 92, 102174 (2020).
Google Scholar 
Jacquemoud, S. & Ustin, S. Leaf Optical Properties (Cambridge Univ. Press, 2019).Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hoffman, M., Koenig, K., Bunting, G., Costanza, J. & Williams, K. J. Biodiversity hotspots (version 2016.1). Zenodo https://doi.org/10.5281/zenodo.3261807 (2016).Folke, C. et al. Resilience thinking: integrating resilience, adaptability and transformability. Ecol. Soc. 15, 20 (2010).
Google Scholar 
Oliver, T. H. et al. Declining resilience of ecosystem functions under biodiversity loss. Nat. Commun. 6, 10122 (2015).Hautier, Y. et al. Anthropogenic environmental changes affect ecosystem stability via biodiversity. Science 348, 336–340 (2015).CAS 
PubMed 

Google Scholar 
Peterson, G., Allen, C. & Holling, C. Ecological resilience, biodiversity, and scale. Ecosystems 1, 6–18 (1998).
Google Scholar 
MacDougall, A. S., McCann, K. S., Gellner, G. & Turkington, R. Diversity loss with persistent human disturbance increases vulnerability to ecosystem collapse. Nature 494, 86–89 (2013).CAS 
PubMed 

Google Scholar 
Duncan, B. N. et al. Space‐based observations for understanding changes in the Arctic‐Boreal Zone. Rev. Geophys. 58, e2019RG000652 (2020).
Google Scholar 
Wittenberg, L., Malkinson, D., Beeri, O., Halutzy, A. & Tesler, N. Spatial and temporal patterns of vegetation recovery following sequences of forest fires in a Mediterranean landscape, Mt. Carmel Israel. CATENA 71, 76–83 (2007).
Google Scholar 
Meng, Y. et al. Analysis of ecological resilience to evaluate the inherent maintenance capacity of a forest ecosystem using a dense Landsat time series. Ecol. Inform. 57, 101064 (2020).
Google Scholar 
Wilson, A. M., Latimer, A. M. & Silander, J. A. Climatic controls on ecosystem resilience: postfire regeneration in the Cape Floristic Region of South Africa. Proc. Natl Acad. Sci. USA 112, 9058 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Xie, Z. et al. Landsat and GRACE observations of arid wetland dynamics in a dryland river system under multi-decadal hydroclimatic extremes. J. Hydrol. 543, 818–831 (2016).Allen, C. R. et al. Quantifying spatial resilience. J. Appl. Ecol. 53, 625–635 (2016).
Google Scholar 
Lausch, A. et al. Understanding and assessing vegetation health by in situ species and remote-sensing approaches. Methods Ecol. Evol. 9, 1799–1809 (2018).
Google Scholar 
Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).CAS 
PubMed 

Google Scholar 
Faruk, A., Belabut, D., Ahmad, N., Knell, R. J. & Garner, T. W. J. Effects of oil-palm plantations on diversity of tropical anurans. Conserv. Biol. 27, 615–624 (2013).PubMed 

Google Scholar 
Yue, S., Brodie, J. F., Zipkin, E. F. & Bernard, H. Oil palm plantations fail to support mammal diversity. Ecol. Appl. 25, 2285–2292 (2015).PubMed 

Google Scholar 
Dislich, C. et al. A review of the ecosystem functions in oil palm plantations, using forests as a reference system. Biol. Rev. Camb. Philos. Soc. 92, 1539–1569 (2017).PubMed 

Google Scholar 
Slingsby, J. A., Moncrieff, G. R. & Wilson, A. M. Near-real time forecasting and change detection for an open ecosystem with complex natural dynamics. ISPRS J. Photogramm. Remote Sens. 166, 15–25 (2020).
Google Scholar 
Spasojevic, M. J. et al. Scaling up the diversity–resilience relationship with trait databases and remote sensing data: the recovery of productivity after wildfire. Glob. Change Biol. 22, 1421–1432 (2016).
Google Scholar 
van der Plas, F. et al. Plant traits alone are poor predictors of ecosystem properties and long-term ecosystem functioning. Nat. Ecol. Evol. 4, 1602–1611 (2020).PubMed 

Google Scholar 
Williams, L. J. et al. Remote spectral detection of biodiversity effects on forest biomass. Nat. Ecol. Evol. 5, 46–54 (2021).PubMed 

Google Scholar 
Schweiger, A. K. et al. Coupling spectral and resource-use complementarity in experimental grassland and forest communities. Proc. R. Soc. B 288, 20211290 (2021).PubMed 
PubMed Central 

Google Scholar 
Isbell, F. I., Polley, H. W. & Wilsey, B. J. Biodiversity, productivity and the temporal stability of productivity: patterns and processes. Ecol. Lett. 12, 443–451 (2009).PubMed 

Google Scholar 
Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591–596 (2001).CAS 
PubMed 

Google Scholar 
Isbell, F., Tilman, D., Reich, P. B. & Clark, A. T. Deficits of biodiversity and productivity linger a century after agricultural abandonment. Nat. Ecol. Evol. 3, 1533–1538 (2019).PubMed 

Google Scholar 
Walters, M. & Scholes, R. The GEO Handbook on Biodiversity Observation Networks (Springer, 2017).Kühl, H. S. et al. Effective biodiversity monitoring needs a culture of integration. One Earth 3, 462–474 (2020).
Google Scholar 
Sasaki, T., Furukawa, T., Iwasaki, Y., Seto, M. & Mori, A. S. Perspectives for ecosystem management based on ecosystem resilience and ecological thresholds against multiple and stochastic disturbances. Ecol. Indic. 57, 395–408 (2015).
Google Scholar 
Thompson, B. K., Olden, J. D. & Converse, S. J. Mechanistic invasive species management models and their application in conservation. Conserv. Sci. Pract. 3, e533 (2021).
Google Scholar 
Lewis, S. L., Edwards, D. P. & Galbraith, D. Increasing human dominance of tropical forests. Science 349, 827–832 (2015).CAS 
PubMed 

Google Scholar 
Ellis, E. C. et al. Used planet: a global history. Proc. Natl Acad. Sci. USA 110, 7978–7985 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
McKey, D. et al. Pre-Columbian agricultural landscapes, ecosystem engineers, and self-organized patchiness in Amazonia. Proc. Natl Acad. Sci. USA 107, 7823–7828 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bush, M. B. et al. A 6900-year history of landscape modification by humans in lowland Amazonia. Quat. Sci. Rev. 141, 52–64 (2016).
Google Scholar 
Wright, J. L. et al. Sixteen hundred years of increasing tree cover prior to modern deforestation in Southern Amazon and Central Brazilian savannas. Glob. Change Biol. 27, 136–150 (2021).
Google Scholar 
Boivin, N. & Crowther, A. Mobilizing the past to shape a better Anthropocene. Nat. Ecol. Evol. 5, 273–284 (2021).PubMed 

Google Scholar 
Malhi, Y., Gardner, T. A., Goldsmith, G. R., Silman, M. R. & Zelazowski, P. Tropical forests in the Anthropocene. Ann. Rev. Environ. Res. 39, 125–159 (2014).Hurtt, G. C. et al. Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6. Geosci. Model Dev. 13, 5425–5464 (2020).CAS 

Google Scholar 
Verburg, P. H., Erb, K.-H., Mertz, O. & Espindola, G. Land system science: between global challenges and local realities. Curr. Opin. Environ. Sustain. 5, 433–437 (2013).PubMed 
PubMed Central 

Google Scholar 
Pendrill, F., Persson, U. M., Godar, J. & Kastner, T. Deforestation displaced: trade in forest-risk commodities and the prospects for a global forest transition. Environ. Res. Lett. 14, 055003 (2019).
Google Scholar 
Burke, M., Driscoll, A., Lobell, D. B. & Ermon, S. Using satellite imagery to understand and promote sustainable development. Science 371, eabe8628 (2021).CAS 
PubMed 

Google Scholar 
Schell, C. J. et al. The ecological and evolutionary consequences of systemic racism in urban environments. Science 369, eaay4497 (2020).Trounstine, J. The geography of inequality: how land use regulation produces segregation. Am. Political Sci. Rev. 114, 443–455 (2020).
Google Scholar 
Su, S., Pi, J., Xie, H., Cai, Z. & Weng, M. Community deprivation, walkability, and public health: highlighting the social inequalities in land use planning for health promotion. Land Use Policy 67, 315–326 (2017).
Google Scholar 
Coomes, O. T., Takasaki, Y. & Rhemtulla, J. M. Forests as landscapes of social inequality tropical forest cover and land distribution among shifting cultivators. Ecol. Soc. 21, 20 (2016).Watmough, G. R. et al. Socioecologically informed use of remote sensing data to predict rural household poverty. Proc. Natl Acad. Sci. USA 116, 1213 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Verburg, P. H. et al. Land system science and sustainable development of the earth system: a global land project perspective. Anthropocene 12, 29–41 (2015).
Google Scholar 
Bickenbach, F., Bode, E., Nunnenkamp, P. & Söder, M. Night lights and regional GDP. Rev. World Econ. 152, 425–447 (2016).
Google Scholar 
Mayer, A. et al. Applying the human appropriation of net primary production framework to map provisioning ecosystem services and their relation to ecosystem functioning across the European Union. Ecosyst. Serv. 51, 101344 (2021).PubMed 
PubMed Central 

Google Scholar 
Li, Y. Urban Green Space Analysis on UBC Vancouver Campus: Integrating Virtual Gaming Technology to Map Cultural Use and Biodiversity Value of Urban Green Space (Univ. British Columbia, 2021).Ghaffarian, S., Roy, D., Filatova, T. & Kerle, N. Agent-based modelling of post-disaster recovery with remote sensing data. Int. J. Disaster Risk Reduct. 60, 102285 (2021).
Google Scholar 
Leclère, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).PubMed 

Google Scholar 
Zeng, Y. et al. Environmental destruction not avoided with the Sustainable Development Goals. Nat. Sustain. 3, 795–798 (2020).
Google Scholar 
Mirza, M. U., Xu, C., Bavel, B. V., van Nes, E. H. & Scheffer, M. Global inequality remotely sensed. Proc. Natl Acad. Sci. USA 118, e1919913118 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kavvada, A. et al. Towards delivering on the Sustainable Development Goals using Earth observations. Remote Sens. Environ. 247, 111930 (2020).
Google Scholar 
Hooper, D. U. & Vitousek, P. M. Effects of plant composition and diversity on nutrient cycling. Ecol. Monogr. 68, 121–149 (1998).
Google Scholar 
Craine, J. M. et al. Global patterns of foliar nitrogen isotopes and their relationships with climate, mycorrhizal fungi, foliar nutrient concentrations, and nitrogen availability. New Phytol. 183, 992 (2009).
Google Scholar 
Madritch, M. D. et al. Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales. Philos. Trans. R. Soc. B 369, 20130194 (2014).
Google Scholar 
Hobbie, S. E. Plant species effects on nutrient cycling: revisiting litter feedbacks. Trends Ecol. Evol. 30, 357–363 (2015).PubMed 

Google Scholar 
Cline, L. C. et al. Resource availability underlies the plant–fungal diversity relationship in a grassland ecosystem. Ecology 99, 204–216 (2018).PubMed 

Google Scholar 
Wardle, D. et al. Ecological linkages between aboveground and belowground biota. Science 304, 1629–1633 (2004).CAS 
PubMed 

Google Scholar 
Meier, C. L. & Bowman, W. D. Links between plant litter chemistry, species diversity, and below-ground ecosystem function. Proc. Natl Acad. Sci. USA 105, 19780–19785 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
Gold, K. M. et al. Hyperspectral measurements enable pre-symptomatic detection and differentiation of contrasting physiological effects of late blight and early blight in potato. Remote Sens. 12, 286 (2020).Serbin, S. P., Singh, A., McNeil, B. E., Kingdon, C. C. & Townsend, P. A. Spectroscopic determination of leaf morphological and biochemical traits for northern temperate and boreal tree species. Ecol. Appl. 24, 1651–1669 (2014).
Google Scholar 
Fisher, J. B., Perakalapudi, N. V., Turner, B. L., Schimel, D. S. & Cusack, D. F. Sci. Rep. 10, 6725 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
van der Heijden, M. G. A., Martin, F. M., Selosse, M.-A. & Sanders, I. R. Mycorrhizal ecology and evolution: the past, the present, and the future. New Phytol. 205, 1406–1423 (2015).PubMed 

Google Scholar 
Meireles, J. E., O’Meara, B. & Cavender-Bares, J. in Remote Sensing of Plant Biodiversity (eds. Cavender-Bares, J. et al.) 155–172 (Springer, 2020).Kothari, S. et al. Community-wide consequences of variation in photoprotective physiology among prairie plants. Photosynthetica 56, 455–467 (2018).CAS 

Google Scholar 
Anderegg, L. D. L. et al. Representing plant diversity in land models: an evolutionary approach to make “functional types” more functional. Glob. Change Biol., https://doi.org/10.1111/gcb.16040 (2022).Cavender-Bares, J. M. et al. Remotely detected aboveground plant function predicts belowground processes in two prairie diversity experiments. Ecol. Monogr., https://doi.org/10.1002/ecm.1488 (2021).Niemann, K. O., Quinn, G., Stephen, R., Visintini, F. & Parton, D. Hyperspectral remote sensing of mountain pine beetle with an emphasis on previsual assessment. Can. J. Remote Sens. 41, 191–202 (2015).
Google Scholar 
Chu, H. et al. Soil microbial biogeography in a changing world: recent advances and future perspectives. mSystems 5, e00803–e00819 (2020).King, G. M. Enhancing soil carbon storage for carbon remediation: potential contributions and constraints by microbes. Trends Microbiol. 19, 75–84 (2011).CAS 
PubMed 

Google Scholar 
Singh, A. K., Sisodia, A., Sisodia, V. & Padhi, M. in New and Future Developments in Microbial Biotechnology and Bioengineering (eds. Singh, J. S. & Singh, D. P.) 57–68 (Elsevier, 2019).Eviner, V. T. Plant traits that influence ecosystem processes vary independently among species. Ecology 85, 2215–2229 (2004).
Google Scholar 
Cornwell, W. K. et al. Plant species traits are the predominant control on litter decomposition rates within biomes worldwide. Ecol. Lett. 11, 1065–1071 (2008).PubMed 

Google Scholar 
Paneque-Gálvez, J. et al. High overlap between traditional ecological knowledge and forest conservation found in the Bolivian Amazon. Ambio 47, 908–923 (2018).PubMed 
PubMed Central 

Google Scholar 
Hilbert, M. The bad news is that the digital access divide is here to stay: domestically installed bandwidths among 172 countries for 1986–2014. Telecommun. Policy 40, 567–581 (2016).
Google Scholar 
Prados, A. I. et al. Impact of the ARSET program on use of remote-sensing data. ISPRS Int. J. Geo-Inf. 8, 261 (2019).Garnett, S. T. et al. A spatial overview of the global importance of Indigenous lands for conservation. Nat. Sustain. 1, 369–374 (2018).
Google Scholar 
Chase, A. S. Z., Chase, D. & Chase, A. Ethics, new colonialism, and lidar data: a decade of lidar in Maya archaeology. J. Comput. Appl. Archaeol. 3, 51–62 (2020).
Google Scholar 
Carrino, T. A., Crósta, A. P., Toledo, C. L. B. & Silva, A. M. Hyperspectral remote sensing applied to mineral exploration in southern Peru: a multiple data integration approach in the Chapi Chiara gold prospect. Int. J. Appl. Earth Obs. Geoinf. 64, 287–300 (2018).
Google Scholar 
Scafutto, R. D. P. M., de Souza Filho, C. R. & de Oliveira, W. J. Hyperspectral remote sensing detection of petroleum hydrocarbons in mixtures with mineral substrates: implications for onshore exploration and monitoring. ISPRS J. Photogramm. Remote Sens. 128, 146–157 (2017).
Google Scholar 
Turner, W. Sensing biodiversity. Science 346, 301–302 (2014).CAS 
PubMed 

Google Scholar 
Ustin, S. L. & Middleton, E. M. Current and near-term advances in Earth observation for ecological applications. Ecol. Process. 10, 1 (2021).Randin, C. F. et al. Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models. Remote Sens. Environ. 239, 111626 (2020).
Google Scholar 
Geller, G. N. et al. in Remote Sensing of Plant Biodiversity (eds. Cavender Bares, J. et al.) 519–526 (Springer, 2020).Asner, G. P. & Martin, R. E. Spectranomics: emerging science and conservation opportunities at the interface of biodiversity and remote sensing. Glob. Ecol. Conserv. 8, 212–219 (2016).
Google Scholar 
Schneider, F. D. et al. Towards mapping the diversity of canopy structure from space with GEDI. Environ. Res. Lett. 15, 115006 (2020).
Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 
Green, R. O. et al. Imaging spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Remote Sens. Environ. 65, 227–248 (1998).
Google Scholar 
Hook, S. & Fisher, J. ECO3ETPTJPL v001 ECOSTRESS Evapotranspiration PT-JPL Daily L3 Global 70 m https://doi.org/10.5067/ECOSTRESS/ECO3ETPTJPL.001 (LP DAAC, accessed 8 December 2021).Turner, A. J. et al. A double peak in the seasonality of California’s photosynthesis as observed from space. Biogeosciences 17, 405–422 (2020).CAS 

Google Scholar 
Radeloff, V. C. et al. The Dynamic Habitat Indices (DHIs) from MODIS and global biodiversity. Remote Sens. Environ. 222, 204–214 (2019).
Google Scholar 
Crameri, F. Scientific colour-maps. Zenodo https://doi.org/10.5281/zenodo.1287763 (2018).Li, X. & Xiao, J. Mapping photosynthesis solely from solar-induced chlorophyll fluorescence: a global, fine-resolution dataset of gross primary production derived from OCO-2. Remote Sensing 11, 2563 (2019).Keil, P. & Chase, J. M. Global patterns and drivers of tree diversity integrated across a continuum of spatial grains. Nat. Ecol. Evol. 3, 390–399 (2019).PubMed 

Google Scholar 
Simard, M., Pinto, N., Fisher, J. B. & Baccini, A. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. Biogeosci. 116, G04021 (2011).Boonman, C. C. F. et al. Assessing the reliability of predicted plant trait distributions at the global scale. Glob. Ecol. Biogeogr. 29, 1034–1051 (2020).PubMed 
PubMed Central 

Google Scholar 
Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. BioScience 51, 933–938 (2001).
Google Scholar 
Beck, H. E. et al. Present and future Köppen–Geiger climate classification maps at 1-km resolution. Sci. Data 5, 180214 (2018).PubMed 
PubMed Central 

Google Scholar 
Mokany, K. et al. Reconciling global priorities for conserving biodiversity habitat. Proc. Natl Acad. Sci. USA 117, 9906 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lausch, A. et al. Linking Earth Observation and taxonomic, structural and functional biodiversity: local to ecosystem perspectives. Ecol. Indic. 70, 317–339 (2016).
Google Scholar 
Schneider, F. D. et al. Mapping functional diversity from remotely sensed morphological and physiological forest traits. Nat. Commun. 8, 1441 (2017).PubMed 
PubMed Central 

Google Scholar 
Rocchini, D. et al. Remotely sensed spectral heterogeneity as a proxy of species diversity: recent advances and open challenges. Ecol. Inform. 5, 318–329 (2010).
Google Scholar 
Schneider, F. D., Ferraz, A. & Schimel, D. Watching Earth’s interconnected systems at work. Eos, https://doi.org/10.1029/2019EO136205 (2019).Laliberté, E., Schweiger, A. K. & Legendre, P. Partitioning plant spectral diversity into alpha and beta components. Ecol. Lett. 23, 370–380 (2020).PubMed 

Google Scholar 
Wang, R. & Gamon, J. A. Remote sensing of terrestrial plant biodiversity. Remote Sens. Environ. 231, 111218 (2019).
Google Scholar 
Féret, J.-B. & Asner, G. P. Mapping tropical forest canopy diversity using high-fidelity imaging spectroscopy. Ecol. Appl. 24, 1289–1296 (2014).PubMed 

Google Scholar 
Dubayah, R. et al. The Global Ecosystem Dynamics Investigation: high-resolution laser ranging of the Earth’s forests and topography. Sci. Remote Sens. 1, 100002 (2020).
Google Scholar 
Omasa, K., Hosoi, F. & Konishi, A. 3D lidar imaging for detecting and understanding plant responses and canopy structure. J. Exp. Bot. 58, 881–898 (2007).CAS 
PubMed 

Google Scholar 
Bae, S. et al. Radar vision in the mapping of forest biodiversity from space. Nat. Commun. 10, 4757 (2019).PubMed 
PubMed Central 

Google Scholar 
Stavros, E. N. et al. ISS observations offer insights into plant function. Nat. Ecol. Evol. 1, 0194 (2017).Turner, W. et al. Free and open-access satellite data are key to biodiversity conservation. Biol. Conserv. 182, 173–176 (2015).
Google Scholar 
Pereira, H. M. et al. Essential biodiversity variables. Science 339, 277–278 (2013).CAS 
PubMed 

Google Scholar 
Jetz, W. et al. Essential biodiversity variables for mapping and monitoring species populations. Nat. Ecol. Evol. 3, 539–551 (2019).PubMed 

Google Scholar 
Kissling, W. D. et al. Towards global data products of essential biodiversity variables on species traits. Nat. Ecol. Evol. 2, 1531–1540 (2018).PubMed 

Google Scholar 
Kissling, W. D. et al. Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale. Biol. Rev. 93, 600–625 (2018).PubMed 

Google Scholar 
Kays, R., Crofoot, M. C., Jetz, W. & Wikelski, M. Terrestrial animal tracking as an eye on life and planet. Science 348, aaa2478 (2015).PubMed 

Google Scholar 
Fretwell, P. T. & Trathan, P. N. Penguins from space: faecal stains reveal the location of emperor penguin colonies. Glob. Ecol. Biogeogr. 18, 543–552 (2009).
Google Scholar 
Davies, A. B. & Asner, G. P. Advances in animal ecology from 3D-LiDAR ecosystem mapping. Trends Ecol. Evol. 29, 681–691 (2014).PubMed 

Google Scholar 
Paz, A. et al. in Remote Sensing of Plant Biodiversity (eds. Cavender-Bares, J. et al.) 255–266 (Springer International Publishing, 2020).Pinto-Ledezma, J. N. & Cavender-Bares, J. Predicting species distributions and community composition using satellite remote sensing predictors. Sci. Rep. 11, 16448 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Papeş, M., Tupayachi, R., Martínez, P., Peterson, A. T. & Powell, G. V. N. Using hyperspectral satellite imagery for regional inventories: a test with tropical emergent trees in the Amazon Basin. J. Veg. Sci. 21, 342–354 (2010).
Google Scholar 
Wang, Z. et al. Mapping foliar functional traits and their uncertainties across three years in a grassland experiment. Remote Sens. Environ. 221, 405–416 (2019).
Google Scholar  More

A biodiversity simulation frameworkWe have developed a simulation framework modelling biodiversity loss to optimize and validate conservation policies (in this context, decisions about data gathering and area protection across a landscape) using an RL algorithm. We implemented a spatially explicit individual-based simulation to assess future biodiversity changes based on natural processes of mortality, replacement and dispersal. Our framework also incorporates anthropogenic processes such as habitat modifications, selective removal of a species, rapid climate change and existing conservation efforts. The simulation can include thousands of species and millions of individuals and track population sizes and species distributions and how they are affected by anthropogenic activity and climate change (for a detailed description of the model and its parameters see Supplementary Methods and Supplementary Table 1).In our model, anthropogenic disturbance has the effect of altering the natural mortality rates on a species-specific level, which depends on the sensitivity of the species. It also affects the total number of individuals (the carrying capacity) of any species that can inhabit a spatial unit. Because sensitivity to disturbance differs among species, the relative abundance of species in each cell changes after adding disturbance and upon reaching the new equilibrium. The effect of climate change is modelled as locally affecting the mortality of individuals based on species-specific climatic tolerances. As a result, more tolerant or warmer-adapted species will tend to replace sensitive species in a warming environment, thus inducing range shifts, contraction or expansion across species depending on their climatic tolerance and dispersal ability.We use time-forward simulations of biodiversity in time and space, with increasing anthropogenic disturbance through time, to optimize conservation policies and assess their performance. Along with a representation of the natural and anthropogenic evolution of the system, our framework includes an agent (that is, the policy maker) taking two types of actions: (1) monitoring, which provides information about the current state of biodiversity of the system, and (2) protecting, which uses that information to select areas for protection from anthropogenic disturbance. The monitoring policy defines the level of detail and temporal resolution of biodiversity surveys. At a minimal level, these include species lists for each cell, whereas more detailed surveys provide counts of population size for each species. The protection policy is informed by the results of monitoring and selects protected areas in which further anthropogenic disturbance is maintained at an arbitrarily low value (Fig. 1). Because the total number of areas that can be protected is limited by a finite budget, we use an RL algorithm42 to optimize how to perform the protecting actions based on the information provided by monitoring, such that it minimizes species loss or other criteria depending on the policy.We provide a full description of the simulation system in the Supplementary Methods. In the sections below we present the optimization algorithm, describe the experiments carried out to validate our framework and demonstrate its use with an empirical dataset.Conservation planning within a reinforcement learning frameworkIn our model we use RL to optimize a conservation policy under a predefined policy objective (for example, to minimize the loss of biodiversity or maximize the extent of protected area). The CAPTAIN framework includes a space of actions, namely monitoring and protecting, that are optimized to maximize a reward R. The reward defines the optimality criterion of the simulation and can be quantified as the cumulative value of species that do not go extinct throughout the timeframe evaluated in the simulation. If the value is set equal across all species, the RL algorithm will minimize overall species extinctions. However, different definitions of value can be used to minimize loss based on evolutionary distinctiveness of species (for example, minimizing phylogenetic diversity loss), or their ecosystem or economic value. Alternatively, the reward can be set equal to the amount of protected area, in which case the RL algorithm maximizes the number of cells protected from disturbance, regardless of which species occur there. The amount of area that can be protected through the protecting action is determined by a budget Bt and by the cost of protection ({C}_{t}^{c}), which can vary across cells c and through time t.The granularity of monitoring and protecting actions is based on spatial units that may include one or more cells and which we define as the protection units. In our system, protection units are adjacent, non-overlapping areas of equal size (Fig. 1) that can be protected at a cost that cumulates the costs of all cells included in the unit.The monitoring action collects information within each protection unit about the state of the system St, which includes species abundances and geographic distribution:$${S}_{t}={{{{H}}}_{{{t}}},{{{D}}}_{{{t}}},{{{F}}}_{{{t}}},{{{T}}}_{{{t}}},{{{C}}}_{{{t}}},{{{P}}}_{{{t}}},{B}_{t}}$$
(1)
where Ht is the matrix with the number of individuals across species and cells, Dt and Ft are matrices describing anthropogenic disturbance on the system, Tt is a matrix quantifying climate, Ct is the cost matrix, Pt is the current protection matrix and Bt is the available budget (for more details see Supplementary Methods and Supplementary Table 1). We define as feature extraction the result of a function X(St), which returns for each protection unit a set of features summarizing the state of the system in the unit. The number and selection of features (Supplementary Methods and Supplementary Table 2) depends on the monitoring policy πX, which is decided a priori in the simulation. A predefined monitoring policy also determines the temporal frequency of this action throughout the simulation, for example, only at the first time step or repeated at each time step. The features extracted for each unit represent the input upon which a protecting action can take place, if the budget allows for it, following a protection policy πY. These features (listed in Supplementary Table 2) include the number of species that are not already protected in other units, the number of rare species and the cost of the unit relative to the remaining budget. Different subsets of these features are used depending on the monitoring policy and on the optimality criterion of the protection policy πY.We do not assume species-specific sensitivities to disturbance (parameters ds, fs in Supplementary Table 1 and Supplementary Methods) to be known features, because a precise estimation of these parameters in an empirical case would require targeted experiments, which we consider unfeasible across a large number of species. Instead, species-specific sensitivities can be learned from the system through the observation of changes in the relative abundances of species (x3 in Supplementary Table 2). The features tested across different policies are specified in the subsection Experiments below and in the Supplementary Methods.The protecting action selects a protection unit and resets the disturbance in the included cells to an arbitrarily low level. A protected unit is also immune from future anthropogenic disturbance increases, but protection does not prevent climate change in the unit. The model can include a buffer area along the perimeter of a protected unit, in which the level of protection is lower than in the centre, to mimic the generally negative edge effects in protected areas (for example, higher vulnerability to extreme weather). Although protecting a disturbed area theoretically allows it to return to its initial biodiversity levels, population growth and species composition of the protected area will still be controlled by the death–replacement–dispersal processes described above, as well as by the state of neighbouring areas. Thus, protecting an area that has already undergone biodiversity loss may not result in the restoration of its original biodiversity levels.The protecting action has a cost determined by the cumulative cost of all cells in the selected protection unit. The cost of protection can be set equal across all cells and constant through time. Alternatively, it can be defined as a function of the current level of anthropogenic disturbance in the cell. The cost of each protecting action is taken from a predetermined finite budget and a unit can be protected only if the remaining budget allows it.Policy definition and optimization algorithmWe frame the optimization problem as a stochastic control problem where the state of the system St evolves through time as described in the section above (see also Supplementary Methods), but it is also influenced by a set of discrete actions determined by the protection policy πY. The protection policy is a probabilistic policy: for a given set of policy parameters and an input state, the policy outputs an array of probabilities associated with all possible protecting actions. While optimizing the model, we extract actions according to the probabilities produced by the policy to make sure that we explore the space of actions. When we run experiments with a fixed policy instead, we choose the action with highest probability. The input state is transformed by the feature extraction function X(St) defined by the monitoring policy, and the features are mapped to a probability through a neural network with the architecture described below.In our simulations, we fix monitoring policy πX, thus predefining the frequency of monitoring (for example, at each time step or only at the first time step) and the amount of information produced by X(St), and we optimize πY, which determines how to best use the available budget to maximize the reward. Each action A has a cost, defined by the function Cost(A, St), which here we set to zero for the monitoring action (X) across all monitoring policies. The cost of the protecting action (Y) is instead set to the cumulative cost of all cells in the selected protection unit. In the simulations presented here, unless otherwise specified, the protection policy can only add one protected unit at each time step, if the budget allows, that is if Cost(Y, St)  More

Graded lockdowns imposed by the South African government to manage the COVID-19 pandemic27,28,29 has afforded us a unique opportunity to quantify shorebird responses to increasing human density in Muizenberg Beach over 8 months in 2020, including a 2-month period of virtual human exclusion. In spite of our study being limited to one beach over 2 years, we were able to take advantage of data collected prior to- (2019) and during the 2020 COVID lockdowns, to better understand a pervasive feature of sandy beach ecosystems (human recreation) that is predicted to intensify in future10.Findings for the 2019–2020 component of our study generally conformed to hypotheses posed. Firstly, shorebird abundance was inversely associated with human abundance and was positively related to lockdown level in 2020. Secondly, shorebird abundance was generally greatest during lockdown levels 5 and 4, when humans were effectively absent from the beach. To contextualise, shorebird abundance was roughly six times greater at the start of lockdown level 5 (2020) than the equivalent period in 2019. Thirdly, lowest shorebird abundance occurred during lockdown level 1 when human abundance was greatest in 2020. Collectively, these findings indicate a strong inverse association between shorebird- and human abundance on Muizenberg Beach and align with results of other studies36,37,38,39. Cumulatively, our findings, allied with prior research highlight the potential for human recreational activity, particularly at high intensities, to impact shorebird utilisation of sandy beach ecosystems, which may in turn affect ecological functions they provide that contribute to ecosystem multifunctionality.The inverse relationship that we recorded between human- and shorebird abundance likely manifests through the diverse ways in which recreational activity impacts fundamental processes and ecosystem components, which in turn link ecologically to shorebirds10,36,37,38,39,40. Muizenberg Beach is popular for surfing, bait-harvesting and general recreational activities, and it is these activities that likely drive the human-shorebird relationship that we report, particularly in 2020. When carried out under high human densities, such activities can lead to a reduction in space available, rendering the ecosystem less suitable as a substrate for birds36. Noise pollution and the presence of dogs may further depress habitat suitability41. Repeated trampling of sediment can negatively impact macrofaunal populations, which together with altered sedimentary biogeochemistry (e.g. increased anoxia), can reduce trophic resource availability to shorebirds, with benthic bait-collecting compounding these effects42,43. At the start of our data collection in 2020, we were unable to identify shorebird species due to lockdown levels 5 and 4 prohibiting human presence on the beach27,28,29. It is probable though that shorebird assemblages during lockdown levels 5 and 4 were not the same as those we identified between lockdown level 3 to 1 (mainly gulls; Table 3). This is based on research showing that increasing environmental disturbances can induce switches in biotic assemblages to those that can tolerate human activities44. Thus, the shorebird assemblages we identified during lockdown levels 3 to 1 is potentially the end-result of the mechanisms highlighted above (space reduction, noise, reduced resource availability) acting on shorebird assemblages in the absence of humans (lockdown levels 5 and 4) following humans being permitted onto the beach.At an inter-annual level, our data revealed idiosyncratic patterns that raise interesting questions about human-shorebird relationships. In 2019, in the absence of any lockdowns, shorebird abundance rose over the winter period (May–August). Winter peaks in abundance have previously been recorded in the literature45,46,47, including for kelp gulls (Larus dominicanus), which were the dominant shorebird in Muizenberg Beach. Specifically, winter abundance peaks for this species have been recorded in sandy beaches in the Eastern Cape, the Swartkops Estuary and Algoa Bay in South Africa (southeast coast)45,46,47. However, the absence of a winter abundance peak in 2020 raises the possibility that the 2019 winter-peak was not seasonal but an opportunistic response to decreased human abundance (see Fig. 4A). In South Africa, coastal ecosystems generally experience greatest human numbers in summer, due to warmer conditions and long end-of-year-vacation periods, based on our observations and experiences.The second inter-annual trend worth noting in our findings is that shorebird abundance was greater in 2019 than 2020, despite lockdowns being implemented in 2020. This counterintuitive finding is likely due to lockdowns that excluded people from the beach in 2020 (levels 5 to 3) being too short in duration to facilitate increases in bird numbers in 2020 beyond the 2019 level. This is supported by our data showing that humans were excluded from the beach for a total of 2 months (April and May 2020; levels 5-4) out of the 8-month period during which photographs were analysed. It would have been expected at the onset of the study that humans would be excluded from the beach during lockdown level 329, which would have resulted in an additional two and a half months of human exclusion and potentially a higher mean shorebird abundance for 2020. However, it is clear from our data that humans were present on the beach during level 3. On closer inspection, it is evident that human numbers increased even prior to the end of lockdown level 4. In fact, human abundance was greater under lockdown level 3 in 2020 than in the same period in 2019. Such high numbers of humans on the beach despite prohibitions are likely due to a lack of compliance, confusion around regulations and/or ‘covid fatigue’, which describes the propensity of humans to grow tired of COVID-19 regulations48. An additional consideration is that human numbers on the beach increased dramatically during lockdown levels 2 and 1, being almost twice the level recorded in 2019 in the same period. The lower 2020 bird count that we recorded is thus likely a product of the short duration of human exclusions in 2020 (lockdown levels 4 and 5) and the magnitude and rate of increase in human numbers thereafter (levels 3-1). Separately, our findings additionally suggest that surrogates (lockdown levels in our case) are unreliable estimators of human presence or abundance and align with findings elsewhere24.The last noteworthy inter-annual trend in our data was the difference in strength of human-shorebird relationships. While the inverse relationship between human and shorebird numbers was evident in both years, it was only during 2020, when humans were excluded from Muizenberg Beach, that the extent of this relationship was revealed. Specifically, in 2020, human exclusion at the start of lockdown level 5 was accompanied by a six-fold increase in shorebird abundance relative to 2019 at the same period. Additional support for the difference in strength of the human-shorebird relationship is the (1) significant interaction recorded between human numbers and year in explaining shorebird abundance and (2) the almost twofold stronger negative relationship (based on regression slopes) between shorebird and human abundance in 2020 vs 2019. These findings suggest that were it not for the COVID lockdowns in 2020, the extent of increasing human numbers on shorebirds may have been masked. However, it must be borne in mind that inter-annual variation may have played some role in the difference in trends recorded for 2019 versus 2020, though we cannot quantify this, given that we only have data for 2 years. Nevertheless, we suggest that when making conservation/management recommendations, decision-makers need to be cognisant of the potential for human effects on sandy beach ecosystems to be underestimated in studies based on variation in human density, in which human exclusion at appropriate spatial and temporal scales is absent24. Concerns have been expressed in the past about the failure of studies to consistently detect large-scale changes in sandy beach ecosystems, including those induced by recreational activities19. We suggest that such deficiencies may relate in part to the scarcity of true human exclusions in disturbance studies at meaningful scales in space and time.Findings from the in situ component of our study suggested that shorebird assemblages were negligibly affected by the transition from lockdown level 3 to 1, but that spatial differences among zones were more prominent. The lack of cases in which lockdown levels interacted statistically with zones (Tables 2, 4) further reinforces our conclusion regarding lockdown effects. Shorebird assemblage structure did vary between lockdown levels 3 and 2, due mainly to increasing contributions of Chroicocephalus hartlaubii (Hartlaub’s Gull) from level 3 to 2 and the opposite for L. dominicanus. Contrary to our hypothesis, differences in assemblage (Shannon–Wiener diversity was the exception) and species metrics were not detected among lockdown levels. This was likely due to the gradient in human abundance being weak among lockdown levels 3 to 1, relative to levels 5 and 4, with there being no virtual exclusion of humans under level 3 lockdown, as would have been expected given government regulations29. It is also possible that under lockdown levels 3, 2 and 1, the shorebird assemblage was simplified and comprised species tolerant of human activities44. The increase in Shannon–Wiener diversity value from lockdown level 3 to 2 was counter expectation, but likely reflects increased evenness during lockdown level 2, brought on by the declining dominance of L. dominicanus and a greater contribution of C. hartlaubii.Taken in its entirety, our findings provide valuable perspectives on human-shorebird interactions in sandy beaches. Based on our 2020 data spanning lockdowns of decreasing severity, our findings suggest that shorebirds are likely to benefit from human-free periods. This benefit is in reality likely to extend across multiple-trophic levels and is unlikely to be shorebird-specific, based on prior research reporting positive organism metrics at lower trophic levels in low human and/or human-free conditions in beach ecosystems20. Broadly, our findings attest to the value of using current and future lockdowns associated with managing the global COVID-19 pandemic to provide data on responses of birds and other organism groups to human-free spaces and times25,26,49. These human-free conditions can additionally provide invaluable data on sensitivities of ecosystem components and processes to increasing human density25,26,49. Data collected during lockdowns can provide better approximations of baseline conditions in sandy beach ecosystems, thereby providing a more meaningful basis for (1) evaluating future ecosystem change in response to human and global change stressors and (2) developing ecosystem restoration programs. This would be central to preventing long-term ecosystem degradation through the shifting base-line syndrome, where successive generations of decision makers/scientists judge the magnitude of change experienced by ecosystem components against increasingly deteriorating conditions over generational time-scales50. We also advocate for data emanating from COVID lockdown studies to be used in public education initiatives, so that beach users are made aware of the ways in which recreational activities can influence beach ecosystems. Such initiatives can improve involvement of public stakeholders in management of sandy beach ecosystems, which has been shown to provide cost-effective and sound decision-making, while increasing support for conservation initiatives51,52,53.Lastly, our findings have shed light on the sensitivity of shorebirds to increasing human numbers, mainly for recreational purposes. By moving beyond binary contrasts of human presence/absence, our work has also shown the magnitude of increasing human numbers on shorebirds, by virtue of the 34.18% increase in human abundance in our study corresponding with a 79.63% decline in bird numbers during the transition from lockdown level 4 to 3 in 2020. This finding is highly relevant considering that our work was based on an urban ecosystem—such systems are thought to have avian communities that are more disturbance tolerant relative to rural or suburban ecosystems54. Broadly, our work emphasises the need for environmental managers and city planners to be cognisant of the sensitivity of shorebirds to human recreational activities, even in urban settings, and to develop appropriate management plans in conjunction with scientists and stakeholders51,52,53. It should be noted that bird responses that we recorded in 2020 are unlikely to be driven solely by changing human numbers in Muizenberg Beach. Processes influencing bird assemblages in beaches surrounding our focal study area, including changes in human numbers and behaviour, may also have been influential determinants of trends recorded. We lack the data to comment meaningfully on this, but is an area worth exploring in future studies.Concluding perspectivesThe global COVID-19 anthropause has been described as the greatest large-scale experiment in modern history. This period has afforded scientists a unique opportunity to refine understanding of the consequences of human activities on Earth’s natural environments25,26,49. This is particularly relevant for human-dominated ecosystems such as sandy beaches, which are arguably the most utilised of Earth’s ecosystems for recreational purposes. In the absence of the COVID-19 anthropause, it is doubtful whether human exclusions could be carried out at scales that would allow meaningful detection of responses to human recreational disturbance. Our findings broadly attest to the points raised thus far, illustrating not only the potential for conventional approaches to underestimate human effects in sandy beaches, but also the sensitivity of shorebirds to human recreation and the magnitude of human influence. We hope that our findings stimulate further research on human recreational effects on sandy beach ecosystems, particularly with a view towards quantifying disturbance sensitivities and response thresholds of fundamental processes that drive multifunctionality in these heavily utilised, yet highly significant coastal ecosystems. We suggest that this is an imperative, given the exponential human population growth expected in the future, particularly along the coast, and the increasing demand predicted on sandy beach ecosystems from recreation, tourism and commercial sectors10,18. At its broadest level, our work dovetails with prior calls for scientists to capitalise on current and future COVID lockdowns to refine our understanding of human-nature interactions25, so that ecosystems and socio-ecological services provided can be sustainably utilised in the future. More

Global patterns of ARG distributionWe used a set of 4572 metagenomic samples to illustrate the global patterns of ARG distribution (Supplementary Data 1). These samples were collected from six types of habitats: air, aquatic, terrestrial, engineered, humans and other hosts (Fig. 1a and Supplementary Data 1). From these samples, we identified a total of 2561 ARGs that conferred resistance to 24 drug classes of antibiotics based on the Comprehensive Antibiotic Research Database (CARD). Of these, 2401 were genes conferring resistance to only one drug class, and 160 conferred resistances to multiple drug classes (Supplementary Data 2). Twenty-five ARGs were found in more than 75% samples, however, the frequency of most ARGs (2313/2561) were More

Alves, J. A. et al. Costs, benefits, and fitness consequences of different migratory strategies. Ecology 94(1), 11–17 (2013).PubMed 

Google Scholar 
Alexander, R. M. When is migration worthwhile for animals that walk, swim or fly?. J. Avian Biol. 29(4), 387–394 (1998).
Google Scholar 
Wikelski, M. et al. Costs of migration in free-flying songbirds. Nature 423, 704 (2003).ADS 
CAS 
PubMed 

Google Scholar 
Alerstam, T., Hedenström, A. & Åkesson, S. Long-distance migration: evolution and determinants. Oikos 103(2), 247–260 (2003).
Google Scholar 
Alerstam, T. Optimal bird migration revisited. J. Ornithol. 152, 5–23 (2011).
Google Scholar 
Hedenstrom, A. & Alerstam, T. Optimum fuel loads in migratory birds: Distinguishing between time and energy minimization. J. Theor. Biol. 189, 227–234 (1997).ADS 
CAS 
PubMed 

Google Scholar 
Åkesson, S. & Helm, B. Endogenous programs and flexibility in bird migration. Front. Ecol. Evol. 8, 78 (2020).
Google Scholar 
Jiguet, F. et al. Desert crossing strategies of migrant songbirds vary between and within species. Sci. Rep. 9(1), 20248 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Senner, N. R., Morbey, Y. E. & Sandercock, B. K. Editorial: Flexibility in the migration strategies of animals. Front. Ecol. Evol. 8, 111 (2020).
Google Scholar 
Mellone, U., López-López, P., Limiñana, R., Piasevoli, G. & Urios, V. The trans-equatorial loop migration system of Eleonora’s falcon: Differences in migration patterns between age classes, regions and seasons. J. Avian Biol. 44, 417–426 (2013).
Google Scholar 
Chevallier, D. et al. Influence of weather conditions on the flight of migrating black storks. Proc. Biol. Sci. 277(1695), 2755–2764 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Verhelst, B., Jansen, J. & Vansteelant, W. South West Georgia: An important bottleneck for raptor migration during autumn. Ardea 99, 137–146 (2011).
Google Scholar 
Klaassen, R. H. G., Strandberg, R., Hake, M. & Alerstam, T. Flexibility in daily travel routines causes regional variation in bird migration speed. Behav. Ecol. Sociobiol. 62(9), 1427–1432 (2008).
Google Scholar 
Alerstam, T. Detours in bird migration. J. Theor. Biol. 209(3), 319–331 (2001).ADS 
CAS 
PubMed 

Google Scholar 
Alerstam, T. & Hedenström, A. The development of bird migration theory. J. Avian Biol. 29(4), 343–369 (1998).
Google Scholar 
Liechti, F., Klaassen, M. & Bruderer, B. Predicting migratory flight altitudes by physiological migration models. Auk 117, 205–214 (2000).
Google Scholar 
Senner, N. R. et al. High-altitude shorebird migration in the absence of topographical barriers: Avoiding high air temperatures and searching for profitable winds. Proc. Biol. Sci. 2018, 285 (1881).
Google Scholar 
Norevik, G., Akesson, S., Andersson, A., Backman, J. & Hedenstrom, A. Flight altitude dynamics of migrating European nightjars across regions and seasons. J. Exp. Biol. 224(20), jeb242836 (2021).PubMed 
PubMed Central 

Google Scholar 
Hadjikyriakou, T. G., Nwankwo, E. C., Virani, M. Z. & Kirschel, A. N. G. Habitat availability influences migration speed, refueling patterns and seasonal flyways of a fly-and-forage migrant. Mov. Ecol. 8, 10 (2020).PubMed 
PubMed Central 

Google Scholar 
Strandberg, R., Klaassen, R. H. G., Olofsson, P. & Alerstam, T. Daily travel schedules of adult Eurasian Hobbies Falco subbuteo—Variability in flight hours and migration speed along the route. Ardea 97(3), 287–295 (2009).
Google Scholar 
Strandberg, R. & Alerstam, T. The strategy of fly-and-forage migration, illustrated for the osprey (Pandion haliaetus). Behav. Ecol. Sociobiol. 61(12), 1865–1875 (2007).
Google Scholar 
McKinnon, E. A. & Love, O. P. Ten years tracking the migrations of small landbirds: Lessons learned in the golden age of bio-logging. Auk 135(4), 834–856 (2018).
Google Scholar 
Backman, J. et al. Actogram analysis of free-flying migratory birds: New perspectives based on acceleration logging. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 203(6–7), 543–564 (2017).PubMed 
PubMed Central 

Google Scholar 
Evens, R., Beenaerts, N., Witters, N. & Artois, T. Study on the foraging behaviour of the European nightjar Caprimulgus europaeus reveals the need for a change in conservation strategy in Belgium. J. Avian Biol. 48(9), 1238–1245 (2017).
Google Scholar 
Evens, R. et al. Lunar synchronization of daily activity patterns in a crepuscular avian insectivore. Ecol. Evol. 10(14), 7106–7116 (2020).PubMed 
PubMed Central 

Google Scholar 
Liechti, F. et al. Miniaturized multi-sensor loggers provide new insight into year-round flight behaviour of small trans-Sahara avian migrants. Mov. Ecol. 6, 19 (2018).PubMed 
PubMed Central 

Google Scholar 
Liechti, F., Witvliet, W., Weber, R. & Bachler, E. First evidence of a 200-day non-stop flight in a bird. Nat. Commun. 4, 2554 (2013).ADS 
PubMed 

Google Scholar 
Dhanjal-Adams, K. L. PAMLr: Suite of functions for manipulating pressure, activity, magnetism and light data in R. (2020).Lisovski, S. et al. Light-level geolocator analyses: A user’s guide. J. Anim. Ecol. 89(1), 221–236 (2020).PubMed 

Google Scholar 
Lisovski, S. et al. Geolocation by light: Accuracy and precision affected by environmental factors. Methods Ecol. Evol. 3(3), 603–612 (2012).
Google Scholar 
Wotherspoon, S., Sumner, M., Lisovski, S. SGAT-Package: Solar/Satellite Geolocation for Animal Tracking. (2021). R package version 0.1.3. GitHub Repository.Bauer, R. RchivalTag: Analyzing Archival Tagging Data. R package version 0.1.2. (2020).Sjöberg, S. et al. Barometer logging reveals new dimensions of individual songbird migration. J. Avian Biol. 49(9), e01821 (2018).
Google Scholar 
Evens, R. et al. Migratory pathways, stopover zones and wintering destinations of Western European Nightjars Caprimulgus europaeus. Ibis 159(3), 680–686 (2017).
Google Scholar 
Becker, J. J. et al. Global bathymetry and elevation data at 30 arc seconds resolution: SRTM30_PLUS. Mar. Geodesy 32(4), 355–371 (2009).
Google Scholar 
Ricketts, T. H. Terrestrial Ecoregions of North America: A Conservation Assessment (Island Press, 1999).
Google Scholar 
Olson, D. M. et al. Terrestrial ecoregions of the world: A new map of life on earth. Bioscience 51(11), 933–938 (2001).
Google Scholar 
QGIS-Development-Team: QGIS Geographic Information System. Open Source Geospatial Foundation (2021).Vansteelant, W. M. G., Gangoso, L., Bouten, W., Viana, D. S. & Figuerola, J. Adaptive drift and barrier-avoidance by a fly-forage migrant along a climate-driven flyway. Mov. Ecol. 9(1), 37 (2021).PubMed 
PubMed Central 

Google Scholar 
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9(2), 378–400 (2017).
Google Scholar 
Hartig, F. DHARMa: Residual Diagnostics for Hierarchical Multi-Level/Mixed) Regression Models. R package version 0.3.3.0. (2020).Lenth, R. emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.5.1. (2020).Akesson, S., Bianco, G. & Hedenstrom, A. Negotiating an ecological barrier: Crossing the Sahara in relation to winds by common swifts. Philos. Trans. R. Soc. Lond. B Biol. Sci. 371(1704), 20150393 (2016).PubMed 
PubMed Central 

Google Scholar 
Strandberg, R., Klaassen, R. H. G., Hake, M., Olofsson, P. & Alerstam, T. Converging migration routes of Eurasian Hobbies Falco subbuteo crossing the African equatorial rain forest. Proc. R. Soc. B 276, 727–733 (2009).PubMed 

Google Scholar 
Rodriguez-Ruiz, J. et al. Disentangling migratory routes and wintering grounds of Iberian near-threatened European Rollers Coracias garrulus. PLoS ONE 9(12), e115615 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Vickery, J. A. et al. The decline of Afro-Palaearctic migrants and an assessment of potential causes. Ibis 156, 1–22 (2014).
Google Scholar 
Evens, R. et al. Proximity of breeding and foraging areas affects foraging effort of a crepuscular, insectivorous bird. Sci. Rep. 8(1), 3008 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Conring, C. M., Brautigam, K., Grisham, B. A., Collins, D. P. & Conway, W. C. Identifying the migratory strategy of the Lower Colorado River Valley population of Greater Sandhill Cranes. Avian Conserv. Ecol. 14(1), 11 (2019).
Google Scholar 
Imlay, T. L., Saldanha, S. & Taylor, P. D. The fall migratory movements of Bank Swallows, Riparia riparia: Fly-and-forage migration?. Avian Conserv. Ecol. 15(1), 2 (2020).
Google Scholar 
Piersma, T. Hop, skip, or jump? Constraints on migration of arctic waders by feeding, fattening, and flight speed. Limosa 60, 185–194 (1987).
Google Scholar 
Warnock, N. Stopping vs. staging: The difference between a hop and a jump. J. Avian Biol. 41(6), 621–626 (2010).
Google Scholar 
Gomez, C. et al. Fuel loads acquired at a stopover site influence the pace of intercontinental migration in a boreal songbird. Sci. Rep. 7(1), 3405 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Tottrup, A. P. et al. The annual cycle of a trans-equatorial Eurasian-African passerine migrant: different spatio-temporal strategies for autumn and spring migration. Proc. Biol. Sci. 279(1730), 1008–1016 (2012).PubMed 

Google Scholar 
Lisovski, S. et al. Inherent limits of light-level geolocation may lead to over-interpretation. Curr. Biol. 28(3), R99–R100 (2018).CAS 
PubMed 

Google Scholar 
Buler, J. J., Moore, F. R. & Woltmann, S. A multi-scale examination of stopover habitat use by birds. Ecology 88(7), 1789–1802 (2007).PubMed 

Google Scholar 
Loon, A. V. et al. Migratory stopover timing is predicted by breeding latitude, not habitat quality, in a long-distance migratory songbird. J. Ornithol. 158(3), 745–752 (2017).
Google Scholar 
Norevik, G. et al. Wind-associated detours promote seasonal migratory connectivity in a flapping flying long-distance avian migrant. J. Anim. Ecol. 89(2), 635–646 (2020).PubMed 

Google Scholar 
Norevik, G., Åkesson, S. & Hedenström, A. Migration strategies and annual space-use in an Afro-Palaearctic aerial insectivore—The European nightjar Caprimulgus europaeus. J. Avian Biol. 48(5), 738–747 (2017).
Google Scholar 
Cresswell, B. & Edwards, D. Geolocators reveal wintering areas of European Nightjar (Caprimulgus europaeus). Bird Study 60(1), 77–86 (2013).
Google Scholar 
Jacobsen, L. B. et al. Annual spatiotemporal migration schedules in three larger insectivorous birds: European nightjar, common swift and common cuckoo. Anim. Biotelem. 5(1), 1–11 (2017).
Google Scholar 
Liechti, F. & Bruderer, B. The relevance of wind for optimal migration theory. J. Avian Biol. 29(4), 561–568 (1998).
Google Scholar 
Schmaljohann, H., Bruderer, B. & Liechti, F. Sustained bird flights occur at temperatures far beyond expected limits. Anim. Behav. 76(4), 1133–1138 (2008).
Google Scholar 
Schmaljohann, H., Liechti, F. & Bruderer, B. Trans-Sahara migrants select flight altitudes to minimize energy costs rather than water loss. Behav. Ecol. Sociobiol. 63(11), 1609–1619 (2009).
Google Scholar 
Sjöberg, S. et al. Extreme altitudes during diurnal flights in a nocturnal songbird migrant. Science 372, 646–648 (2021).ADS 
PubMed 

Google Scholar 
Bruderer, B., Peter, D. & Korner-Nievergelt, F. Vertical distribution of bird migration between the Baltic Sea and the Sahara. J. Ornithol. 159(2), 315–336 (2018).
Google Scholar  More

Barton, N. Evolutionary biology. The geometry of adaptation. Nature 395, 751–752. https://doi.org/10.1038/27338 (1998).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Otto, S. P. & Lenormand, T. Resolving the paradox of sex and recombination. Nat. Rev. Genet. 3, 252–261. https://doi.org/10.1038/nrg761 (2002).CAS 
Article 
PubMed 

Google Scholar 
Becks, L. & Agrawal, A. F. Higher rates of sex evolve in spatially heterogeneous environments. Nature 468, 89–92. https://doi.org/10.1038/nature09449 (2010).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83. https://doi.org/10.1126/science.aan8048 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Thompson, D. M. & van Woesik, R. Corals escape bleaching in regions that recently and historically experienced frequent thermal stress. Proc. Biol. Sci. 276, 2893–2901. https://doi.org/10.1098/rspb.2009.0591 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Pandolfi, J. M., Connolly, S. R., Marshall, D. J. & Cohen, A. L. Projecting coral reef futures under global warming and ocean acidification. Science 333, 418–422. https://doi.org/10.1126/science.1204794 (2011).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Sully, S., Burkepile, D. E., Donovan, M. K., Hodgson, G. & van Woesik, R. A global analysis of coral bleaching over the past two decades. Nat. Commun. 10, 1264. https://doi.org/10.1038/s41467-019-09238-2 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Yund, P. O. How severe is sperm limitation in natural populations of marine free-spawners?. Trends Ecol. Evol. 15, 10–13 (2000).CAS 
Article 

Google Scholar 
Levitan, D. R. & Petersen, C. Sperm limitation in the sea. Trend Ecol. Evol. 10, 228–231 (1995).CAS 
Article 

Google Scholar 
Baird, A., Guest, J. & Willis, B. Systematic and biogeographical patterns in the reproductive biology of scleractinian corals. Annu. Rev. Ecol. Evol. Syst. 40, 551–571. https://doi.org/10.1146/Annurev.Ecolsys.110308.120220 (2009).Article 

Google Scholar 
Wei, N. V. et al. Reproductive isolation among Acropora species (Scleractinia: Acroporidae) in a marginal coral assemblage. Zool. Stud. 51, 85–92 (2012).
Google Scholar 
Kitanobo, S., Isomura, N., Fukami, H., Iwao, K. & Morita, M. The reef-building coral Acropora conditionally hybridize under sperm limitation. Biol. Lett. 12, 20160511. https://doi.org/10.1098/rsbl.2016.0511 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Mercier, A. & Hamel, J.-F. Synchronized breeding events in sympatric marine invertebrates: Role of behavior and fine temporal windows in maintaining reproductive isolation. Behav. Ecol. Sociobiol. 64, 1749–1765 (2010).Article 

Google Scholar 
Levitan, D. R. et al. Mechanisms of reproductive isolation among sympatric broadcast-spawning corals of the Montastraea annularis species complex. Evolution 58, 308–323 (2004).Article 

Google Scholar 
Willis, B. L., Babcock, R. C., Harrison, P. L. & Wallace, C. C. Experimental hybridization and breeding incompatibilities within the mating systems of mass spawning reef corals. Coral Reefs 16, S53–S65 (1997).Article 

Google Scholar 
Nozawa, Y., Isomura, N. & Fukami, H. Influence of sperm dilution and gamete contact time on the fertilization rate of scleractinian corals. Coral Reefs 34, 1199–1206. https://doi.org/10.1007/s00338-015-1338-3 (2015).ADS 
Article 

Google Scholar 
Oliver, J. & Babcock, R. Aspects of the fertilization ecology of broadcast spawning corals: Sperm dilution effects and in situ measurements of fertilization. Biol. Bull. 183, 409–417. https://doi.org/10.2307/1542017 (1992).CAS 
Article 
PubMed 

Google Scholar 
Coma, R. & Lasker, H. R. Small-scale heterogeneity of fertilization success in a broadcast spawning octocoral. J. Exp. Mar. Biol. Ecol. 214, 107–120. https://doi.org/10.1016/S0022-0981(97)00017-8 (1997).Article 

Google Scholar 
Teo, A. & Todd, P. A. Simulating the effects of colony density and intercolonial distance on fertilisation success in broadcast spawning scleractinian corals. Coral Reefs 37, 891–900. https://doi.org/10.1007/s00338-018-1715-9 (2018).ADS 
Article 

Google Scholar 
Marshall, D. J. In situ measures of spawning synchrony and fertilization success in an intertidal, free-spawning invertebrate. Mar. Ecol. Prog. Ser. 236, 113–119 (2002).ADS 
Article 

Google Scholar 
Babcock, R. C., Mundy, C. N. & Whitehead, D. Sperm diffusion-models and in-situ confirmation of long-distance fertilization in the free-spawning asteroid Acanthaster planci. Biol. Bull. 186, 17–28 (1994).CAS 
Article 

Google Scholar 
Omori, M., Fukami, H., Kobinata, H. & Hatta, M. Significant drop of fertilization of Acropora corals in 1999. An after-effect of heavy coral bleaching?. Limnol. Oceanogr. 46, 704–706. https://doi.org/10.4319/lo.2001.46.3.0704 (2001).ADS 
Article 

Google Scholar 
Levitan, D. R., Fogarty, N. D., Jara, J., Lotterhos, K. E. & Knowlton, N. Genetic, spatial, and temporal components of precise spawning synchrony in reef building corals of the Montastraea annularis species complex. Evolution 65, 1254–1270. https://doi.org/10.1111/j.1558-5646.2011.01235.x (2011).Article 
PubMed 

Google Scholar 
Fukami, H., Omori, M., Shimoike, K., Hayashibara, T. & Hatta, M. Ecological and genetic aspects of reproductive isolation by different spawning times in Acropora corals. Mar. Biol. 142, 679–684. https://doi.org/10.1007/S00227-002-1001-8 (2003).Article 

Google Scholar 
Morita, M. et al. Reproductive strategies in the intercrossing corals Acropora donei and A. tenuis to prevent hybridization. Coral Reefs 38, 1211–1223. https://doi.org/10.1007/s00338-019-01839-z (2019).ADS 
Article 

Google Scholar 
Shinzato, C. et al. Development of novel, cross-species microsatellite markers for Acropora corals using next-generation sequencing technology. Front. Mar. Sci. 1, 11 (2014).Article 

Google Scholar 
R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020).Albright, R. & Mason, B. Projected near-future levels of temperature and pCO2 reduce coral fertilization success. PLoS One 8, e56468. https://doi.org/10.1371/journal.pone.0056468 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Iguchi, A., Morita, M., Nakajima, Y., Nishikawa, A. & Miller, D. In vitro fertilization efficiency in coral Acropora digitifera. Zygote 17, 225–227. https://doi.org/10.1017/S096719940900519X (2009).Article 
PubMed 

Google Scholar 
Morita, M. et al. Eggs regulate sperm flagellar motility initiation, chemotaxis and inhibition in the coral Acropora digitifera, A. gemmifera and A. tenuis. J. Exp. Biol. 209, 4574–4579. https://doi.org/10.1242/jeb.02500 (2006).CAS 
Article 
PubMed 

Google Scholar 
Chan, W. Y., Hoffmann, A. A. & van Oppen, M. J. H. Hybridization as a conservation management tool. Conserv. Lett. https://doi.org/10.1111/conl.12652 (2019).Article 

Google Scholar  More